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Looking Back at 2018 APM Predictions - Did They Come True? Part 2

Jonah Kowall
Spacelift

I would like to highlight some of the predictions made at the start of 2018, and how those have panned out, or not actually occurred. I will review some of the predictions and trends from APMdigest's 2018 APM Predictions. Here is Part 2: Start with Looking Back at 2018 APM Predictions - Did They Come True? Part 1

Microtrends

The use of chatbots and other connected assistants have not yielded any benefits in the IT Operations space in 2018, but there are many emerging startups in this area looking to change that over the coming years. While several companies such as ServiceNow and Microsoft made acquisitions in this market, they haven’t produced anything tangible, especially not in 2018. Time will tell if these are a passing fad or they become a cornerstone of computing. IoT is still nascent, especially in the APM market. The predictions about its growing importance and adoption of APM for IoT are still generally immature and early stage. There are some incredible stories for those doing this, but it’s still a very small number today. Those predictions around IoT are likely too early. Similarly, Blockchain doesn’t even go there, way too early considering how few real implementations of Blockchain are implemented in production at this point. Maybe in another five years, we can begin to make some predictions, but it will likely be longer before Blockchain performance management solutions are needed by the market.

Culture and Communication

The biggest barrier to transformation is culture and people. This has been clear from every major CIO survey conducted in the last 10 years of economic growth in this bull market. Our communications and the way we do incident response have evolved significantly. The players in this space are solving an extremely important problem, one which MAY change the culture of an organization. This trend will continue as these technologies become essential to better communication of increasingly distributed workforces. The codification of the role of the SRE by the excellent second book from Google has helped the industry understand how to apply DevOps in an even more concrete manner. The predictions about SRE were spot on, as SRE has become the gold standard for managing and operating applications. Still early for most organizations, but now on the radar. There were several predictions about SRE for the past year. I would, however, say that the vendors who predicted DevOps and culture change by a tool were sadly far from reality. Tools don’t change cultures, but cultural changes often require tool changes. Wrapping up a great 2018, I wish everyone a productive and creative 2019 where we can listen, learn, innovate, share, and advance our group of APM vendors and practitioners. There are many problems to solve, and new approaches being invented daily by this amazing community.

Jonah Kowall is SVP of Product and Design at Spacelift

Hot Topics

The Latest

For decades, traditional observability tools have forced IT professionals to sift through endless dashboards, turning a flood of alerts into actionable insight and driving widespread burnout. While enterprises adopted AI to eliminate this friction, most still deploy it reactively rather than proactively ... As systems become more complex and IT moves to the center of business strategy, a reactive posture is unsustainable ...

Ask an operations team what they monitor and you will hear a familiar list: latency, errors, throughput, uptime. Those signals still matter for AI, but they no longer answer the most important question. As Krishna Sai, CTO of SolarWinds, puts it: "Traditional observability tells you a system is running. AI observability tells you whether it's still reasoning soundly. Those are completely different questions" ...

AI observability serves several roles across an organization. AI engineers use it to build, evaluate, and improve AI systems. SREs use it to operate those systems reliably and efficiently in production, including managing their cost. Leaders need it to connect AI behavior and operational performance to business outcomes. The common need is a clear understanding of whether AI systems are delivering the intended result ...

Part 4 outlines the challenges enterprise face when adopting AI observability. The main challenge is that AI complexity compounds at every stage of maturity. LLM-based applications introduce new behavior, dependencies, and quality considerations, while Agentic AI adds further failure modes as systems make decisions and take actions ...

Part 3 reports on the current state of AI observability. Most experts agree: enterprises are moving faster with AI than they are with the operational discipline needed to run it safely ...

When discussing AI Observability, one of the first questions that arises: Is this just an extension of traditional observability or is this a whole new type of tool? Part 2 compares traditional observability with AI observability ...

In this extensive 14-part series that will post over the next three weeks, experts across observability and AI will provide insight and opinions on the need for AI Observability, the challenges, the benefits, the current state, and predictions for the future ...

Amid rising complexity, organizations are moving quickly toward agent-driven operations: AgenticOps, according to The Impact of Agentic AI on Network Operations, a new report from Cisco ...

AI, automation, and enterprise software give organizations an extraordinary amount of leverage. Work that once required hours can happen in minutes. Information buried across systems can be analyzed almost instantly. Complex workflows can be coordinated across teams with far less manual intervention. That leverage creates a temptation to start with the technology ...

Everyone wants to talk about models. Compute clusters. Benchmark scores that climb a fraction of a point and make headlines anyway. It's an easy conversation to have because it's measurable. But it's also increasingly beside the point ...

Looking Back at 2018 APM Predictions - Did They Come True? Part 2

Jonah Kowall
Spacelift

I would like to highlight some of the predictions made at the start of 2018, and how those have panned out, or not actually occurred. I will review some of the predictions and trends from APMdigest's 2018 APM Predictions. Here is Part 2: Start with Looking Back at 2018 APM Predictions - Did They Come True? Part 1

Microtrends

The use of chatbots and other connected assistants have not yielded any benefits in the IT Operations space in 2018, but there are many emerging startups in this area looking to change that over the coming years. While several companies such as ServiceNow and Microsoft made acquisitions in this market, they haven’t produced anything tangible, especially not in 2018. Time will tell if these are a passing fad or they become a cornerstone of computing. IoT is still nascent, especially in the APM market. The predictions about its growing importance and adoption of APM for IoT are still generally immature and early stage. There are some incredible stories for those doing this, but it’s still a very small number today. Those predictions around IoT are likely too early. Similarly, Blockchain doesn’t even go there, way too early considering how few real implementations of Blockchain are implemented in production at this point. Maybe in another five years, we can begin to make some predictions, but it will likely be longer before Blockchain performance management solutions are needed by the market.

Culture and Communication

The biggest barrier to transformation is culture and people. This has been clear from every major CIO survey conducted in the last 10 years of economic growth in this bull market. Our communications and the way we do incident response have evolved significantly. The players in this space are solving an extremely important problem, one which MAY change the culture of an organization. This trend will continue as these technologies become essential to better communication of increasingly distributed workforces. The codification of the role of the SRE by the excellent second book from Google has helped the industry understand how to apply DevOps in an even more concrete manner. The predictions about SRE were spot on, as SRE has become the gold standard for managing and operating applications. Still early for most organizations, but now on the radar. There were several predictions about SRE for the past year. I would, however, say that the vendors who predicted DevOps and culture change by a tool were sadly far from reality. Tools don’t change cultures, but cultural changes often require tool changes. Wrapping up a great 2018, I wish everyone a productive and creative 2019 where we can listen, learn, innovate, share, and advance our group of APM vendors and practitioners. There are many problems to solve, and new approaches being invented daily by this amazing community.

Jonah Kowall is SVP of Product and Design at Spacelift

Hot Topics

The Latest

For decades, traditional observability tools have forced IT professionals to sift through endless dashboards, turning a flood of alerts into actionable insight and driving widespread burnout. While enterprises adopted AI to eliminate this friction, most still deploy it reactively rather than proactively ... As systems become more complex and IT moves to the center of business strategy, a reactive posture is unsustainable ...

Ask an operations team what they monitor and you will hear a familiar list: latency, errors, throughput, uptime. Those signals still matter for AI, but they no longer answer the most important question. As Krishna Sai, CTO of SolarWinds, puts it: "Traditional observability tells you a system is running. AI observability tells you whether it's still reasoning soundly. Those are completely different questions" ...

AI observability serves several roles across an organization. AI engineers use it to build, evaluate, and improve AI systems. SREs use it to operate those systems reliably and efficiently in production, including managing their cost. Leaders need it to connect AI behavior and operational performance to business outcomes. The common need is a clear understanding of whether AI systems are delivering the intended result ...

Part 4 outlines the challenges enterprise face when adopting AI observability. The main challenge is that AI complexity compounds at every stage of maturity. LLM-based applications introduce new behavior, dependencies, and quality considerations, while Agentic AI adds further failure modes as systems make decisions and take actions ...

Part 3 reports on the current state of AI observability. Most experts agree: enterprises are moving faster with AI than they are with the operational discipline needed to run it safely ...

When discussing AI Observability, one of the first questions that arises: Is this just an extension of traditional observability or is this a whole new type of tool? Part 2 compares traditional observability with AI observability ...

In this extensive 14-part series that will post over the next three weeks, experts across observability and AI will provide insight and opinions on the need for AI Observability, the challenges, the benefits, the current state, and predictions for the future ...

Amid rising complexity, organizations are moving quickly toward agent-driven operations: AgenticOps, according to The Impact of Agentic AI on Network Operations, a new report from Cisco ...

AI, automation, and enterprise software give organizations an extraordinary amount of leverage. Work that once required hours can happen in minutes. Information buried across systems can be analyzed almost instantly. Complex workflows can be coordinated across teams with far less manual intervention. That leverage creates a temptation to start with the technology ...

Everyone wants to talk about models. Compute clusters. Benchmark scores that climb a fraction of a point and make headlines anyway. It's an easy conversation to have because it's measurable. But it's also increasingly beside the point ...